Building computational models is one of the best ways of furthering our understanding of both the normal function of the nervous system and the pathology associated with aging, neural trauma, or disease. Biophysically detailed models provide a framework for integrating data across spatial scales and for exploring hypotheses about the biological mechanisms underlying neuronal and network dynamics. However, as models increase in complexity, additional barriers emerge to the creation, validation, exchange and re-use of models. The NeuroML project aims to address these issues by providing a standard format for describing multiscale models in neuroscience. NeuroML is supported by over 30 tools and databases and is the basis for model exchange at Open Source Brain, where 374 users are collaborating on 57 public modeling projects. In spite of this promising movement toward model sharing in the neuroscience community, it is extremely rare to see a specific, rigorous statement of the criteria used for evaluating models during model development, and multiple models for the same ion channels and neurons are not compared for concordance with the same suite of experimental data. The overall goal of this project is to create a flexible infrastructure for assessing the scope and quality of computational models in neuroscience and to make this information broadly available to the community for a large class of models. Aim 1 focuses on enhancing existing tools to work together seamlessly for validation of NeuroML models against experimental data. Aim 2 concentrates on the development of a dedicated web portal, incorporation of automated model validation into existing model sharing platforms, and the creation of documentation, tutorials, forums and other outreach for promoting uptake and obtaining user feedback. Aim 3 includes testing of the validation tool chain in multiple large-scale neuronal network modeling environments. The proposed activities will build bridges that connect multiple, existing initiatives in support of model development, validation, exchange, selection, and re-use, and will integrate experimental data with modeling efforts for more efficiency, better transparency, and greater impact of computational models in neuroscience research.